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CNN–BiLSTM Attention Hybrid Modeling Czochralski Silicon Diameter Prediction #WorldResearchAwards

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Introduction High-precision prediction of crystal diameter during the growth of electronic-grade silicon single crystals is a crucial requirement for achieving superior crystal quality and yield in semiconductor manufacturing . The Czochralski (Cz) crystal growth process operates under extreme thermal conditions and exhibits strong nonlinear behavior, time-delay effects , and sensitivity to external disturbances. These challenges significantly restrict the prediction accuracy of traditional mechanism-based models, which rely on simplified heat-transfer principles and geometric assumptions. As industrial demand for larger and defect-free silicon wafers continues to rise, advanced predictive strategies capable of handling complex process dynamics have become increasingly essential. Limitations of Mechanism-Based Diameter Models Mechanism-based models in crystal growth typically describe the relationship between heater power, pulling rate, and crystal diameter through physics-informe...
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Quantum Imaging with Metasurfaces: Gains, Limits & Future Prospects #WorldResearchAwards

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  Introduction Quantum imaging exploits nonclassical properties of light—such as entanglement and photon correlations —to overcome classical limits in resolution, sensitivity, and noise suppression. While these advantages promise transformative impacts in precision sensing and microscopy, real-world implementation has been hindered by bulky optical components and limited system flexibility. The emergence of metasurfaces provides a powerful pathway to bridge this gap, enabling compact, scalable, and highly controllable quantum imaging architectures suitable for next-generation research and technology. Metasurfaces for Quantum Wavefront Engineering Metasurfaces are ultrathin optical platforms composed of subwavelength nanostructures that allow precise control of phase, amplitude, and polarization. In quantum imaging, they enable deterministic manipulation of single photons and entangled states , replacing conventional lenses and beam-shaping elements. This wavefront engineerin...
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The Role of Reduced Surface Sulfur Species in Se(VI) Removal #WorldResearchAwards

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Introduction Sulfidized nano zero-valent iron (S-nZVI) has emerged as a highly effective material for the reductive removal of toxic oxyanions from aqueous environments. Its enhanced performance compared to pristine nZVI is attributed to improved electron selectivity and conductivity between the Fe(0) core and target contaminants. Sulfidation modifies the particle surface, introducing reactive sulfur species that fundamentally alter redox behavior . While S-nZVI has been widely studied for contaminant removal, the specific role of these sulfur species has remained insufficiently explored. Understanding their contribution is essential for optimizing S-nZVI design and predicting its behavior in both engineered and natural sulfidic systems. Mechanism of Se(VI) Reduction by S-nZVI The reduction of Se(VI) by S-nZVI occurs at significantly enhanced rates, up to ten times faster than with non-sulfidized nZVI. This acceleration is strongly influenced by the molar S/Fe ratio and selenium co...
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Miniaturized High-Speed FBG Interrogator on Photonic AWG Chip #Photonics #WorldResearchAwards

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Introduction Arrayed waveguide gratings (AWGs) play a central role in fiber Bragg grating (FBG) interrogation systems, yet conventional AWG-based interrogators are typically bulky, complex, and difficult to deploy in harsh or space-constrained environments. These limitations restrict their applicability in advanced sensing scenarios requiring portability, robustness, and real-time response. This research addresses these challenges by presenting a highly miniaturized FBG interrogator based on a photonic AWG chip, designed to deliver high precision and high-speed wavelength demodulation for demanding engineering applications. Photonic AWG Chip Design and Miniaturization The core of the proposed system is an ultra-compact photonic AWG chip measuring only 280 µm × 150 µm. This chip-level integration significantly reduces system size while maintaining high spectral resolution. By embedding the AWG within a compact photonic chip module, the interrogator achieves substantial miniaturizati...
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Distinguished Scientist Award | Honoring Global Research Excellence #WorldResearchAwards

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Introduction The Distinguished Scientist Award represents one of the highest forms of recognition in the global research ecosystem , honoring scientists whose work has significantly advanced knowledge, innovation, and societal progress. It celebrates researchers who consistently demonstrate excellence through original thinking, rigorous methodology, and impactful outcomes that shape the future of science and technology . This award highlights not only individual achievement but also the broader value of research in addressing complex global challenges. Research excellence and innovation At the core of the Distinguished Scientist Award is a strong emphasis on research excellence and innovation. Award recipients are recognized for groundbreaking discoveries, high-impact publications, and pioneering approaches that redefine existing scientific paradigms . Their research often opens new directions within their disciplines, introduces novel methodologies, and accelerates scientific progr...
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Advanced Nonlinear & Learning-Based Control for Complex Systems #WorldResearchAwards

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Introduction The rapid evolution of modern engineering systems has intensified the need for advanced modeling and control strategies capable of addressing complexity, uncertainty, and strong nonlinearities . Learning-based and nonlinear control methods have emerged as powerful tools for dealing with such challenges, enabling improved performance, robustness, and adaptability. Recent research highlights the integration of machine learning , optimization, and adaptive techniques as a promising pathway to overcome the limitations of classical control approaches in complex dynamical systems . Learning-Based Control for Complex Dynamics Learning-based control approaches leverage data-driven models and machine learning algorithms to capture unknown or partially known system dynamics. By continuously learning from real-time data, these methods can adapt to changing environments and uncertainties. Techniques such as reinforcement learning , neural-network-based controllers , and hybrid mo...
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Hybrid AI–Taguchi–ANOVA for Thermographic Monitoring of Electronic Devices #WorldResearchAwards

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  Introduction Printed circuit boards (PCBs) are fundamental to modern electronic systems, and their reliability directly affects the performance and safety of critical applications. Undetected defects in PCBs can evolve gradually, leading to unexpected failures and costly downtime. Conventional monitoring techniques, often limited to simulations or surface-level measurements, lack the capability for real-time fault detection and predictive maintenance. This research addresses these limitations by introducing an integrated framework that combines infrared thermography (IRT), artificial intelligence (AI), and Taguchi–ANOVA statistical methods to enable accurate, real-time diagnosis of thermal anomalies in operating PCBs . Infrared Thermography for Real-Time PCB Monitoring Infrared thermography serves as a non-contact, non-destructive technique for capturing thermal signatures of PCBs during normal operation. By visualizing heat distribution and thermal stresses , IRT reveals hi...
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